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--- |
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tags: |
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- ALE/MontezumaRevenge-v5 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- custom-implementation |
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library_name: cleanrl |
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model-index: |
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- name: DQN |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: ALE/MontezumaRevenge-v5 |
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type: ALE/MontezumaRevenge-v5 |
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metrics: |
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- type: mean_reward |
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value: 0.00 +/- 0.00 |
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name: mean_reward |
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verified: false |
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--- |
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# (CleanRL) **DQN** Agent Playing **ALE/MontezumaRevenge-v5**
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This is a trained model of a DQN agent playing ALE/MontezumaRevenge-v5.
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The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
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found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/Montezuma.py).
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## Get Started
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To use this model, please install the `cleanrl` package with the following command:
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```
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pip install "cleanrl[Montezuma]"
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python -m cleanrl_utils.enjoy --exp-name Montezuma --env-id ALE/MontezumaRevenge-v5
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```
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Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
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## Command to reproduce the training
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```bash
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curl -OL https://huggingface.co/cotran2/Montezuma/raw/main/dqn_atari.py
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curl -OL https://huggingface.co/cotran2/Montezuma/raw/main/pyproject.toml
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curl -OL https://huggingface.co/cotran2/Montezuma/raw/main/poetry.lock
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poetry install --all-extras
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python dqn_atari.py --exp-name Montezuma --track --wandb-project-name Montezuma --capture-video --env-id ALE/MontezumaRevenge-v5 --total-timesteps 500000 --buffer-size 400000 --save-model True --upload-model True --hf-entity cotran2
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```
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# Hyperparameters
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```python
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{'batch_size': 32,
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'buffer_size': 400000,
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'capture_video': True,
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'cuda': True,
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'end_e': 0.01,
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'env_id': 'ALE/MontezumaRevenge-v5',
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'exp_name': 'Montezuma',
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'exploration_fraction': 0.1,
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'gamma': 0.99,
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'hf_entity': 'cotran2',
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'learning_rate': 0.0001,
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'learning_starts': 80000,
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'num_envs': 1,
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'save_model': True,
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'seed': 1,
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'start_e': 1,
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'target_network_frequency': 1000,
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'tau': 1.0,
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'torch_deterministic': True,
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'total_timesteps': 500000,
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'track': True,
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'train_frequency': 4,
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'upload_model': True,
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'wandb_entity': None,
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'wandb_project_name': 'Montezuma'}
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```
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